Automated promotion forecasting and methods therefor

ABSTRACT

Methods and apparatus for implementing forward looking optimizing promotions by administering, in large numbers and iteratively, test promotions formulated using highly granular test variables on purposefully segmented subpopulations. The plurality of test promotions are automatically proposed. The responses from individuals in the subpopulations are received and analyzed. The analysis result is employed to subsequently formulate a general public promotion.

PRIORITY CLAIM

The present invention is a continuation-in-part of a commonly owned USpatent application entitled “Architecture and Methods for PromotionOptimization,” U.S. application Ser. No. 14/209,851, Attorney Docket No.PRCO-P001, filed in the USPTO on Mar. 13, 2014, by inventor Moran, whichclaims priority under 35 U.S.C. 119(e) to a commonly owned USprovisional patent application entitled “Architecture and Methods forPromotion Optimization,” U.S. Application No. 61/780,630, AttorneyDocket No. PRCO-P001P1, filed in the USPTO on Mar. 13, 2013, by inventorMoran, all of which is incorporated herein by reference.

RELATED APPLICATIONS

The present invention is related to the following applications, all ofwhich are incorporated herein by reference:

Commonly owned application entitled “Adaptive Experimentation andOptimization in Automated Promotional Testing,” application Ser. No.______, filed herewith in the USPTO by Moran et al. (Attorney DocketNumber PRCO-P002).

Commonly owned application entitled “Automated and Optimal PromotionalExperimental Test Designs Incorporating Constraints,” application Ser.No. ______, filed herewith in the USPTO by Moran et al. (Attorney DocketNumber PRCO-P003).

Commonly owned application entitled “Automatic Offer Generation UsingConcept Generator Apparatus and Methods Therefor,” application Ser. No.______, filed herewith in the USPTO by Moran et al. (Attorney DocketNumber PRCO-P004).

Commonly owned application entitled “Automated Event Correlation toImprove Promotional Testing,” application Ser. No. ______, filedherewith in the USPTO by Moran et al. (Attorney Docket NumberPRCO-P005).

Commonly owned application entitled “Automated Behavioral EconomicsPatterns in Promotion Testing and Methods Therefor,” application Ser.No. ______, filed herewith in the USPTO by Moran et al. (Attorney DocketNumber PRCO-P007).

BACKGROUND OF THE INVENTION

The present invention relates to promotion optimization methods andapparatus therefor. More particularly, the present invention relates tocomputer-implemented methods and computer-implemented apparatus foroptimizing promotions.

Promotion refers to various practices designed to increase sales of aparticular product or services and/or the profit associated with suchsales. Generally speaking, the public often associates promotion withthe sale of consumer goods and services, including consumer packagedgoods (e.g., food, home and personal care), consumer durables (e.g.,consumer appliances, consumer electronics, automotive leasing), consumerservices (e.g., retail financial services, health care, insurance, homerepair, beauty and personal care), and travel and hospitality (e.g.,hotels, airline flights, and restaurants). Promotion is particularlyheavily involved in the sale of consumer packaged goods (i.e., consumergoods packaged for sale to an end consumer). However, promotion occursin almost any industry that offers goods or services to a buyer (whetherthe buyer is an end consumer or an intermediate entity between theproducer and the end consumer).

The term promotion may refer to, for example, providing discounts (usingfor example a physical or electronic coupon or code) designed to, forexample, promote the sales volume of a particular product or service.One aspect of promotion may also refer to the bundling of goods orservices to create a more desirable selling unit such that sales volumemay be improved. Another aspect of promotion may also refer to themerchandising design (with respect to looks, weight, design, color,etc.) or displaying of a particular product with a view to increasingits sales volume. It includes calls to action or marketing claims usedin-store, on marketing collaterals, or on the package to drive demand.Promotions may be composed of all or some of the following: price basedclaims, secondary displays or aisle end-caps in a retail store, shelfsignage, temporary packaging, placement in a retailercircular/flyer/coupon book, a colored price tag, advertising claims, orother special incentives intended to drive consideration and purchasebehavior. These examples are meant to be illustrative and not limiting.

In discussing various embodiments of the present invention, the sale ofconsumer packaged goods (hereinafter “CPG”) is employed to facilitatediscussion and ease of understanding. It should be kept in mind,however, that the promotion optimization methods and apparatusesdiscussed herein may apply to any industry in which promotion has beenemployed in the past or may be employed in the future.

Further, price discount is employed as an example to explain thepromotion methods and apparatuses herein. It should be understood,however, that promotion optimization may be employed to manipulatefactors other than price discount in order to influence the salesvolume. An example of such other factors may include the call to actionon a display or on the packaging, the size of the CPG item, the mannerin which the item is displayed or promoted or advertised either in thestore or in media, etc.

Generally speaking, it has been estimated that, on average, 17% of therevenue in the consumer packaged goods (CPG) industry is spent to fundvarious types of promotions, including discounts, designed to enticeconsumers to try and/or to purchase the packaged goods. In a typicalexample, the retailer (such as a grocery store) may offer a discountonline or via a print circular to consumers. The promotion may bespecifically targeted to an individual consumer (based on, for example,that consumer's demographics or past buying behavior). The discount mayalternatively be broadly offered to the general public. Examples ofpromotions offered to general public include for example, a printed orelectronic redeemable discount (e.g., coupon or code) for a specific CPGitem. Another promotion example may include, for example, generaladvertising of the reduced price of a CPG item in a particulargeographic area. Another promotion example may include in-store markingdown of a particular CPG item only for a loyalty card user base.

In an example, if the consumer redeems the coupon or electronic code,the consumer is entitled to a reduced price for the CPG item. Therevenue loss to the retailer due to the redeemed discount may bereimbursed, wholly or partly, by the manufacturer of the CPG item in aseparate transaction.

Because promotion is expensive (in terms of, for example, the effort toconduct a promotion campaign and/or the per-unit revenue loss to theretailer/manufacturer when the consumer decides to take advantage of thediscount), efforts are continually made to minimize promotion cost whilemaximizing the return on promotion dollars investment. This effort isknown in the industry as promotion optimization.

For example, a typical promotion optimization method may involveexamining the sales volume of a particular CPG item over time (e.g.,weeks). The sales volume may be represented by a demand curve as afunction of time, for example. A demand curve lift (excess overbaseline) or dip (below baseline) for a particular time period would beexamined to understand why the sales volume for that CPG item increasesor decreases during such time period.

FIG. 1 shows an example demand curve 102 for Brand X cookies over someperiod of time. Two lifts 110 and 114 and one dip 112 in demand curve102 are shown in the example of FIG. 1. Lift 110 shows that the demandfor Brand X cookies exceeds the baseline at least during week 2. Byexamining the promotion effort that was undertaken at that time (e.g.,in the vicinity of weeks 1-4 or week 2) for Brand X cookies, marketershave in the past attempted to judge the effectiveness of the promotioneffort on the sales volume. If the sales volume is deemed to have beencaused by the promotion effort and delivers certain financialperformance metrics, that promotion effort is deemed to have beensuccessful and may be replicated in the future in an attempt to increasethe sales volume. On the other hand, dip 112 is examined in an attemptto understand why the demand falls off during that time (e.g., weeks 3and 4 in FIG. 1). If the decrease in demand was due to the promotion inweek 2 (also known as consumer pantry loading or retailerforward-buying, depending on whether the sales volume shown reflects thesales to consumers or the sales to retailers), this decrease in weeks 3and 4 should be counted against the effectiveness of week 2.

One problem with the approach employed in the prior art has been thefact that the prior art approach is a backward-looking approach based onaggregate historical data. In other words, the prior art approachattempts to ascertain the nature and extent of the relationship betweenthe promotion and the sales volume by examining aggregate data collectedin the past. The use of historical data, while having some disadvantages(which are discussed later herein below), is not necessarily a problem.However, when such data is in the form of aggregate data (such as insimple terms of sales volume of Brand X cookies versus time for aparticular store or geographic area), it is impossible to extract fromsuch aggregate historical data all of the other factors that may morelogically explain a particular lift or dip in the demand curve.

To elaborate, current promotion optimization approaches tend to evaluatesales lifts or dips as a function of four main factors: discount depth(e.g., how much was the discount on the CPG item), discount duration(e.g., how long did the promotion campaign last), timing (e.g., whetherthere was any special holidays or event or weather involved), andpromotion type (e.g., whether the promotion was a price discount only,whether Brand X cookies were displayed/not displayed prominently,whether Brand X cookies were features/not featured in the promotionliterature).

However, there may exist other factors that contribute to the sales liftor dip, and such factors are often not discoverable by examining, in abackward-looking manner, the historical aggregate sales volume data forBrand X cookies. This is because there is not enough information in theaggregate sales volume data to enable the extraction of informationpertaining to unanticipated or seemingly unrelated events that may havehappened during the sales lifts and dips and may have actuallycontributed to the sales lifts and dips.

Suppose, for example, that there was a discount promotion for Brand Xcookies during the time when lift 110 in the demand curve 102 happens.However, during the same time, there was a breakdown in the distributionchain of Brand Y cookies, a competitor's cookies brand which manyconsumers view to be an equivalent substitute for Brand X cookies. WithBrand Y cookies being in short supply in the store, many consumersbought Brand X instead for convenience sake. Aggregate historical salesvolume data for Brand X cookies, when examined after the fact inisolation by Brand X marketing department thousands of miles away, wouldnot uncover that fact. As a result, Brand X marketers may make themistaken assumption that the costly promotion effort of Brand X cookieswas solely responsible for the sales lift and should be continued,despite the fact that it was an unrelated event that contributed to mostof the lift in the sales volume of Brand X cookies.

As another example, suppose, for example, that milk produced by aparticular unrelated vendor was heavily promoted in the same grocerystore or in a different grocery store nearby during the week that BrandX cookies experienced the sales lift 110. The milk may have beenhighlighted in the weekly circular, placed in a highly visible locationin the store and/or a milk industry expert may have been present in thestore to push buyers to purchase milk, for example. Many consumers endedup buying milk because of this effort whereas some of most of thoseconsumers who bought during the milk promotion may have waited anotherweek or so until they finished consuming the milk they bought in theprevious weeks. Further, many of those milk-buying consumers during thisperiod also purchased cookies out of an ingrained milk-and-cookieshabit. Aggregate historical sales volume data for Brand X cookies wouldnot uncover that fact unless the person analyzing the historicalaggregate sales volume data for Brand X cookies happened to be presentin the store during that week and had the insight to note that milk washeavily promoted that week and also the insight that increased milkbuying may have an influence on the sales volume of Brand X cookies.

Software may try to take these unanticipated events into account butunless every SKU (stock keeping unit) in that store and in stores withincommuting distance and all events, whether seemingly related orunrelated to the sales of Brand X cookies, are modeled, it is impossibleto eliminate data noise from the backward-looking analysis based onaggregate historical sales data.

Even without the presence of unanticipated factors, a marketing personworking for Brand X may be interested in knowing whether the relativelymodest sales lift 114 comes from purchases made by regular Brand Xcookies buyers or by new buyers being enticed by some aspect of thepromotion campaign to buy Brand X cookies for the first time. If Brand Xmarketer can ascertain that most of the lift in sales during thepromotion period that spans lift 114 comes from new consumers of Brand Xcookies, such marketer may be willing to spend more money on the sametype of sales promotion, even to the point of tolerating a negative ROI(return on investment) on his promotion dollars for this particular typeof promotion since the recruitment of new buyers to a brand is deemedmore much valuable to the company in the long run than the temporaryincrease in sales to existing Brand X buyers. Again, aggregatehistorical sales volume data for Brand X cookies, when examined in abackward-looking manner, would not provide such information.

Furthermore, even if all unrelated and related events and factors can bemodeled, the fact that the approach is backward-looking means that thereis no way to validate the hypothesis about the effect an event has onthe sales volume since the event has already occurred in the past. Withrespect to the example involving the effect of milk promotion on Brand Xcookies sales, there is no way to test the theory short of duplicatingthe milk shortage problem again. Even if the milk shortage problem couldbe duplicated again for testing purposes, other conditions have changed,including the fact that most consumers who bought milk during thatperiod would not need to or be in a position to buy milk again in a longtime. Some factors, such as weather, cannot be duplicated, making theoryverification challenging.

Attempts have been made to employ non-aggregate sales data in promotingproducts. For example, some companies may employ a loyalty card program(such as the type commonly used in grocery stores or drug stores) tokeep track of purchases by individual consumers. If an individualconsumer has been buying sugar-free cereal, for example, themanufacturer of a new type of whole grain cereal may wish to offer adiscount to that particular consumer to entice that consumer to try outthe new whole grain cereal based on the theory that people who boughtsugar-free cereal tend to be more health conscious and thus more likelyto purchase whole grain cereal than the general cereal-consuming public.Such individualized discount may take the form of, for example, aredeemable discount such as a coupon or a discount code mailed oremailed to that individual.

Some companies may vary the approach by, for example, ascertaining theitems purchased by the consumer at the point of sale terminal andoffering a redeemable code on the purchase receipt. Irrespective of theapproach taken, the utilization of non-aggregate sales data hastypically resulted in individualized offers, and has not been processedor integrated in any meaningful sense into a promotion optimizationeffort to determine the most cost-efficient, highest-return manner topromote a particular CPG item to the general public.

Attempts have also been made to obtain from the consumers themselvesindications of future buying behavior instead of relying on abackward-looking approach. For example, conjoint studies, one of thestated preference methods, have been attempted in which consumers areasked to state preferences. In an example conjoint study, a consumer maybe approached at the store and asked a series of questions designed touncover the consumer's future shopping behavior when presented withdifferent promotions. Questions may be asked include, for example, “doyou prefer Brand X or Brand Y” or “do you spend less than $100 or morethan $100 weekly on grocery” or “do you prefer chocolate cookies oroatmeal cookies” or “do you prefer a 50-cent-off coupon or a 2-for-1deal on cookies”. The consumer may state his preference on each of thequestions posed (thus making this study a conjoint study on statedpreference).

However, such conjoint studies have proven to be an expensive way toobtain non-historical data. If the conjoint studies are presented via acomputer, most users may ignore the questions and/or refuse toparticipate. If human field personnel are employed to talk to individualconsumers to conduct the conjoint study, the cost of such studies tendsto be quite high due to salary cost of the human field personnel and maymake the extensive use of such conjoint studies impractical.

Further and more importantly, it has been known that conjoint studiesare somewhat unreliable in gauging actual purchasing behavior byconsumers in the future. An individual may state out of guilt and theknowledge that he needs to lose weight that he will not purchase anycookies in the next six months, irrespective of discounts. In actuality,that individual may pick up a package of cookies every week if suchpackage is carried in a certain small size that is less guilt-inducingand/or if the package of cookies is prominently displayed next to themilk refrigerator and/or if a 10% off discount coupon is available. If apromotion effort is based on such flawed stated preference data,discounts may be inefficiently deployed in the future, costing themanufacturer more money than necessary for the promotion.

Finally, none of the approaches track the long-term impact of apromotion's effect on brand equity for an individual's buying behaviorover time. Some promotions, even if deemed a success by traditionalshort-term measures, could have damaging long-term consequences.Increased price-based discounting, for example, can lead to consumersincreasing the weight of price in determining their purchase decisions,making consumers more deal-prone and reluctant to buy at full price,leading to less loyalty to brands and retail outlets.

In view of the foregoing, there are desired improved methods andapparatuses for optimizing promotions in a manner that results incost-effective, high-return, and timely promotions to the generalpublic.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings and in whichlike reference numerals refer to similar elements and in which:

FIG. 1 shows an example demand curve 102 for Brand X cookies over someperiod of time.

FIG. 2A shows, in accordance with an embodiment of the invention, aconceptual drawing of the forward-looking promotion optimization method.

FIG. 2B shows, in accordance with an embodiment of the invention, thesteps for generating a general public promotion.

FIG. 3A shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the user'sperspective.

FIG. 3B shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the forward-lookingpromotion optimization system perspective.

FIG. 4 shows various example segmentation criteria that may be employedto generate the purposefully segmented subpopulations.

FIG. 5 shows various example methods for communicating the testpromotions to individuals of the segmented subpopulations being tested.

FIG. 6 shows, in accordance with an embodiment, various examplepromotion-significant responses.

FIG. 7 shows, in accordance with an embodiment of the invention, variousexample test promotion variables affecting various aspects of a typicaltest promotion.

FIG. 8 shows, in accordance with an embodiment of the invention, ageneral hardware/network view of a forward-looking promotionoptimization system.

FIG. 9 shows, in accordance with an embodiment, a conceptual high-levelview of the promotion forecaster.

FIG. 10 shows an example experimental knowledge base.

FIG. 11 shows, in accordance with an embodiment of the invention, thepromotion forecaster of FIG. 9 in greater detail.

FIG. 12 shows, in accordance with an embodiment of the invention, thesteps for generating, in an automated manner and with improved accuracy,a promotion that has an improved likelihood of success based on dataobtained from past experimental promotions.

DETAILED DESCRIPTION OF EMBODIMENTS

The present invention will now be described in detail with reference toa few embodiments thereof as illustrated in the accompanying drawings.In the following description, numerous specific details are set forth inorder to provide a thorough understanding of the present invention. Itwill be apparent, however, to one skilled in the art, that the presentinvention may be practiced without some or all of these specificdetails. In other instances, well known process steps and/or structureshave not been described in detail in order to not unnecessarily obscurethe present invention.

Various embodiments are described herein below, including methods andtechniques. It should be kept in mind that the invention might alsocover articles of manufacture that includes a computer readable mediumon which computer-readable instructions for carrying out embodiments ofthe inventive technique are stored. The computer readable medium mayinclude, for example, semiconductor, magnetic, opto-magnetic, optical,or other forms of computer readable medium for storing computer readablecode. Further, the invention may also cover apparatuses for practicingembodiments of the invention. Such apparatus may include circuits,dedicated and/or programmable, to carry out tasks pertaining toembodiments of the invention. Examples of such apparatus include any ofthe data processing devices, including for example smart phones, tabletcomputers, laptop computers, or a general-purpose computers and/ordedicated computing devices when appropriately programmed and mayinclude a combination of a computer/computing device anddedicated/programmable circuits adapted for the various tasks pertainingto embodiments of the invention. Such a data processing device include,as is well-known, at least a processor unit, a memory unit, a graphicprocessing unit, a data storage unit (such as a hard drive orsemiconductor-based data storage device), one or more I/O circuits, oneor more data communication sub-systems, and/or operatingsystem/applications for executing executable code. Data processingdevices are well-known and are not discussed in greater detail hereinfor brevity's sake. The apparatuses may be stand-alone or may be coupledtogether using a network, such as a local area network, an intranet, aninternet, or a combination thereof.

One or more embodiments of the invention relate to methods and apparatusfor optimizing promotions by administering, in large numbers anditeratively, test promotions on purposefully segmented subpopulations inadvance of a general public promotion roll-out. In one or moreembodiments, the inventive forward-looking promotion optimization(FL-PO) involves obtaining actual revealed preferences from individualconsumers of the segmented subpopulations being tested.

The revealed preferences are obtained when the individual consumersrespond to specifically designed actual test promotions. The revealedpreferences are tracked in individual computer-implemented accounts(which may, for example, be implemented via a record in a centralizeddatabase and rendered accessible to the merchant or the consumer via acomputer network such as the internet) associated with individualconsumers. For example, when a consumer responds, using his smart phoneor web browser, to a test promotion that offers 20% off a particularconsumer packaged goods (CPG) item, that response is tracked in hisindividual computer-implemented account. Such computer-implementedaccounts may be implemented via, for example, a loyalty card program,apps on a smart phone, computerized records accessible via a browser,social media news feed, etc.

In one or more embodiments, a plurality of test promotions may bedesigned and tested on a plurality of groups of consumers (the groups ofconsumers are referred to herein as “subpopulations”). The responses bythe consumers are recorded and analyzed, with the analysis resultemployed to generate additional test promotions or to formulate thegeneral population promotion.

As will be discussed later herein, if the consumer actually redeems theoffer, one type of response is recorded and noted in thecomputer-implemented account of that consumer. Even if an action by theconsumer does not involve actually redeeming or actually takingadvantage of the promotional offer right away, an action by thatconsumer may, however, constitute a response that indicates a level ofinterest or lack of interest and may still be useful in revealing theconsumer preference (or lack thereof). For example, if a consumer savesan electronic coupon (offered as part of a test promotion) in hiselectronic coupon folder or forwards that coupon to a friend via anemail or a social website, that action may indicate a certain level ofinterest and may be useful in determining the effectiveness of a giventest promotion. Different types of responses by the consumers may beaccorded different weights, in one or more embodiments.

The groups of consumers involved in promotion testing represent segmentsof the public that have been purposefully segmented in accordance withsegmenting criteria specifically designed for the purpose of testing thetest promotions. As the term is employed herein, a subpopulation isdeemed purposefully segmented when its members are selected based oncriteria other than merely to make up a given number of members in thesubpopulation. Demographics, buying behavior, behavioral economics areexample criteria that may be employed to purposefully segment apopulation into subpopulations for promotion testing. In an example, asegmented population may number in the tens or hundreds or eventhousands of individuals. In contrast, the general public may involvetens of thousands, hundreds of thousands, or millions of potentialcustomers.

By purposefully segmenting the public into small subpopulations forpromotion testing, embodiments of the invention can exert control overvariables such as demographics (e.g., age, income, sex, marriage status,address, etc.), buying behavior (e.g., regular purchaser of Brand Xcookies, consumer of premium food, frequent traveler, etc), weather,shopping habits, life style, and/or any other criteria suitable for usein creating the subpopulations. More importantly, the subpopulations arekept small such that multiple test promotions may be executed ondifferent subpopulations, either simultaneously or at different times,without undue cost or delay in order to obtain data pertaining to thetest promotion response behavior. The low cost/low delay aspect ofcreating and executing test promotions on purposefully segmentedsubpopulations permits, for example, what-if testing, testing instatistically significant numbers of tests, and/or iterative testing toisolate winning features in test promotions.

Generally speaking, each individual test promotion may be designed totest one or more test promotion variables. These test promotionsvariables may relate to, for example, the size, shape, color, manner ofdisplay, manner of discount, manner of publicizing, manner ofdissemination pertaining to the goods/services being promoted.

As a very simple example, one test promotion may involve 12-oz packagesof fancy-cut potato chips with medium salt and a discount of 30% off theregular price. This test promotion may be tested on a purposefullysegmented subpopulation of 35-40 years old professionals in the$30,000-$50,000 annual income range. Another test promotion may involvethe same 30% discount 12-oz packages of fancy-cut potato chips withmedium salt on a different purposefully segmented subpopulation of 35-40years old professionals in the higher $100,000-$150,000 annual incomerange. By controlling all variables except for income range, theresponses of these two test promotions, if repeated in statisticallysignificant numbers, would likely yield fairly accurate informationregarding the relationship between income for 35-40 years oldprofessionals and their actual preference for 12-oz packages of fancycut potato chips with medium salt.

In designing different test promotions, one or more of the testpromotions variables may vary or one or more of the segmenting criteriaemployed to create the purposefully segmented subpopulations may vary.The test promotion responses from individuals in the subpopulations arethen collected and analyzed to ascertain which test promotion or testpromotion variable(s) yields/yield the most desirable response (based onsome predefined success criteria, for example).

Further, the test promotions can also reveal insights regarding whichsubpopulation performs the best or well with respect to test promotionresponses. In this manner, test promotion response analysis providesinsights not only regarding the relative performance of the testpromotion and/or test promotion variable but also insights regardingpopulation segmentation and/or segmentation criteria. In an embodiment,it is contemplated that the segments may be arbitrarily or randomlysegmented into groups and test promotions may be executed against thesearbitrarily segmented groups in order to obtain insights regardingpersonal characteristics that respond well to a particular type ofpromotion.

In an embodiment, the identified test promotion variable(s) that yieldthe most desirable responses may then be employed to formulate a generalpublic promotion (GPP), which may then be offered to the larger public.A general public promotion is different from a test promotion in that ageneral public promotion is a promotion designed to be offered tomembers of the public to increase or maximize sales or profit whereas atest promotion is designed to be targeted to a small group ofindividuals fitting a specific segmentation criteria for the purpose ofpromotion testing. Examples of general public promotions include (butnot limited to) advertisement printed in newspapers, release in publicforums and websites, flyers for general distribution, announcement onradios or television, and/or promotion broadly transmitted or madeavailable to members of the public. The general public promotion maytake the form of a paper or electronic circular that offers the samepromotion to the larger public, for example.

Alternatively or additionally, promotion testing may be iterated overand over with different subpopulations (segmented using the same ordifferent segmenting criteria) and different test promotions (devisedusing the same or different combinations of test promotion variables) inorder to validate one or more the test promotion response analysisresult(s) prior to the formation of the generalized public promotion. Inthis manner, “false positives” may be reduced.

Since a test promotion may involve many test promotion variables,iterative test promotion testing, as mentioned, may help pin-point avariable (i.e., promotion feature) that yields the most desirable testpromotion response to a particular subpopulation or to the generalpublic.

Suppose, for example, that a manufacturer wishes to find out the mosteffective test promotion for packaged potato chips. One test promotionmay reveal that consumers tend to buy a greater quantity of potato chipswhen packaged in brown paper bags versus green paper bags. That“winning” test promotion variable value (i.e., brown paper bagpackaging) may be retested in another set of test promotions usingdifferent combinations of test promotion variables (such as for examplewith different prices, different display options, etc.) on the same ordifferent purposefully segmented subpopulations. The follow-up testpromotions may be iterated multiple times in different test promotionvariable combinations and/or with different test subpopulations tovalidate that there is, for example, a significant consumer preferencefor brown paper bag packaging over other types of packaging for potatochips.

Further, individual “winning” test promotion variable values fromdifferent test promotions may be combined to enhance the efficacy of thegeneral public promotion to be created. For example, if a 2-for-1discount is found to be another winning variable value (e.g., consumerstend to buy a greater quantity of potato chips when offered a 2-for-1discount), that winning test promotion variable value (e.g., theaforementioned 2-for-1 discount) of the winning test promotion variable(e.g., discount depth) may be combined with the brown paper packagingwinning variable value to yield a promotion that involves discounting2-for-1 potato chips in brown paper bag packaging.

The promotion involving discounting 2-for-1 potato chips in brown paperbag packaging may be tested further to validate the hypothesis that sucha combination elicits a more desirable response than the response fromtest promotions using only brown paper bag packaging or from testpromotions using only 2-for-1 discounts. As many of the “winning” testpromotion variable values may be identified and combined in a singlepromotion as desired. At some point, a combination of “winning” testpromotion variables (involving one, two, three, or more “winning” testpromotion variables) may be employed to create the general publicpromotion, in one or more embodiments.

In one or more embodiments, test promotions may be executed iterativelyand/or in a continual fashion on different purposefully segmentedsubpopulations using different combinations of test promotion variablesto continue to obtain insights into consumer actual revealedpreferences, even as those preferences change over time. Note that theconsumer responses that are obtained from the test promotions are actualrevealed preferences instead of stated preferences. In other words, thedata obtained from the test promotions administered in accordance withembodiments of the invention pertains to what individual consumersactually do when presented with the actual promotions. The data istracked and available for analysis and/or verification in individualcomputer-implemented accounts of individual consumers involved in thetest promotions. This revealed preference approach is opposed to astated preference approach, which stated preference data is obtainedwhen the consumer states what he would hypothetically do in response to,for example, a hypothetically posed conjoint test question.

As such, the actual preference test promotion response data obtained inaccordance with embodiments of the present invention is a more reliableindicator of what a general population member may be expected to behavewhen presented with the same or a similar promotion in a general publicpromotion. Accordingly, there is a closer relationship between the testpromotion response behavior (obtained in response to the testpromotions) and the general public response behavior when a generalpublic promotion is generated based on such test promotion responsedata.

Also, the lower face validity of a stated preference test, even if theinsights have statistical relevance, poses a practical challenge; CPGmanufacturers who conduct such tests have to then communicate theinsights to a retailer in order to drive real-world behavior, andconvincing retailers of the validity of these tests after the fact canlead to lower credibility and lower adoption, or “signal loss” as thetop concepts from these tests get re-interpreted by a third party, theretailer, who wasn't involved in the original test design.

It should be pointed out that embodiments of the inventive testpromotion optimization methods and apparatuses disclosed herein operateon a forward-looking basis in that the plurality of test promotions aregenerated and tested on segmented subpopulations in advance of theformulation of a general public promotion. In other words, the analysisresults from executing the plurality of test promotions on differentpurposefully segmented subpopulations are employed to generate futuregeneral public promotions. In this manner, data regarding the “expected”efficacy of the proposed general public promotion is obtained evenbefore the proposed general public promotion is released to the public.This is one key driver in obtaining highly effective general publicpromotions at low cost.

Furthermore, the subpopulations can be generated with highly granularsegmenting criteria, allowing for control of data noise that may arisedue to a number of factors, some of which may be out of the control ofthe manufacturer or the merchant. This is in contrast to the aggregateddata approach of the prior art.

For example, if two different test promotions are executed on twosubpopulations shopping at the same merchant on the same date,variations in the response behavior due to time of day or trafficcondition are essentially eliminated or substantially minimized in theresults (since the time or day or traffic condition would affect the twosubpopulations being tested in substantially the same way).

The test promotions themselves may be formulated to isolate specifictest promotion variables (such as the aforementioned potato chip brownpaper packaging or the 16-oz size packaging). This is also in contrastto the aggregated data approach of the prior art.

Accordingly, individual winning promotion variables may be isolated andcombined to result in a more effective promotion campaign in one or moreembodiments. Further, the test promotion response data may be analyzedto answer questions related to specific subpopulation attribute(s) orspecific test promotion variable(s). With embodiments of the invention,it is now possible to answer, from the test subpopulation response data,questions such as “How deep of a discount is required to increase by 10%the volume of potato chip purchased by buyers who are 18-25 year-oldmale shopping on a Monday?” or to generate test promotions specificallydesigned to answer such a question. Such data granularity and analysisresult would have been impossible to achieve using the backward-looking,aggregate historical data approach of the prior art.

In one or more embodiments, there is provided a promotional idea modulefor generating ideas for promotional concepts to test. The promotionalidea generation module relies on a series of pre-constructed sentencestructures that outline typical promotional constructs. For example, BuyX, get Y for $Z price would be one sentence structure, whereas Get Y for$Z when you buy X would be a second. It's important to differentiatethat the consumer call to action in those two examples is materiallydifferent, and one cannot assume the promotional response will be thesame when using one sentence structure vs. another. The solution isflexible and dynamic, so once X, Y, and Z are identified, multiple validsentence structures can be tested. Additionally, other variables in thesentence could be changed, such as replacing “buy” with “hurry up andbuy” or “act now” or “rush to your local store to find”. The solutiondelivers a platform where multiple products, offers, and different waysof articulating such offers can be easily generated by a lay user. Theamount of combinations to test can be infinite. Further, the generationmay be automated, saving time and effort in generating promotionalconcepts.

In one or more embodiments, once a set of concepts is developed, thetechnology advantageously a) will constrain offers to only test “viablepromotions”, i.e., those that don't violate local laws, conflict withbranding guidelines, lead to unprofitable concepts that wouldn't bepractically relevant, can be executed on a retailers' system, etc.,and/or b) link to the design of experiments for micro-testing todetermine which combinations of variables to test at any given time.

In one or more embodiments, there is provided an offer selection modulefor enabling a non-technical audience to select viable offers for thepurpose of planning traditional promotions (such as general populationpromotion, for example) outside the test environment. By using filtersand advanced consumer-quality graphics, the offer selection module willbe constrained to only show top performing concepts from the tests, withproduction-ready artwork wherever possible. By doing so, the offerselection module renders irrelevant the traditional, Excel-based orheavily numbers-oriented performance reports from traditional analytictools. The user can have “freedom within a framework” by selecting anyof the pre-scanned promotions for inclusion in an offer to the generalpublic, but value is delivered to the retailer or manufacturer becausethe offers are constrained to only include the best performing concepts.Deviation from the top concepts can be accomplished, but only once thespecific changes are run through the testing process and emerge in theoffer selection windows.

In an embodiment, it is expressly contemplated that the generalpopulation and/or subpopulations may be chosen from social media site(e.g., Facebook™, Twitter™, Google+™, etc.) participants. Social mediaoffers a large population of active participants and often providevarious communication tools (e.g., email, chat, conversation streams,running posts, etc.) which makes it efficient to offer promotions and toreceive responses to the promotions. Various tools and data sourcesexist to uncover characteristics of social media site members, whichcharacteristics (e.g., age, sex, preferences, attitude about aparticular topic, etc.) may be employed as highly granular segmentationcriteria, thereby simplifying segmentation planning.

Although grocery stores and other brick-and-mortar businesses arediscussed in various examples herein, it is expressly contemplated thatembodiments of the invention apply also to online shopping and onlineadvertising/promotion and online members/customers.

These and other features and advantages of embodiments of the inventionmay be better understood with reference to the figures and discussionsthat follow.

FIG. 2A shows, in accordance with an embodiment of the invention, aconceptual drawing of the forward-looking promotion optimization method.As shown in FIG. 2A, a plurality of test promotions 102 a, 102 b, 102 c,102 d, and 102 e are administered to purposefully segmentedsubpopulations 104 a, 104 b, 104 c, 104 d, and 104 e respectively. Asmentioned, each of the test promotions (102 a-102 e) may be designed totest one or more test promotion variables.

In the example of FIG. 2A, test promotions 102 a-102 d are shown testingthree test promotion variables X, Y, and Z, which may represent forexample the size of the packaging (e.g., 12 oz versus 16 oz), the mannerof display (e.g., at the end of the aisle versus on the shelf), and thediscount (e.g., 10% off versus 2-for-1). These promotion variables areof course only illustrative and almost any variable involved inproducing, packaging, displaying, promoting, discounting, etc. of thepackaged product may be deemed a test promotion variable if there is aninterest in determining how the consumer would respond to variations ofone or more of the test promotion variables. Further, although only afew test promotion variables are shown in the example of FIG. 2A, a testpromotion may involve as many or as few of the test promotion variablesas desired. For example, test promotion 102 e is shown testing four testpromotion variables (X, Y, Z, and T).

One or more of the test promotion variables may vary from test promotionto test promotion. In the example of FIG. 2A, test promotion 102 ainvolves test variable X1 (representing a given value or attribute fortest variable X) while test promotion 102 b involves test variable X2(representing a different value or attribute for test variable X). Atest promotion may vary, relative to another test promotion, one testpromotion variable (as can be seen in the comparison between testpromotions 102 a and 102 b) or many of the test promotion variables (ascan be seen in the comparison between test promotions 102 a and 102 d).Also, there are no requirements that all test promotions must have thesame number of test promotion variables (as can be seen in thecomparison between test promotions 102 a and 102 e) although for thepurpose of validating the effect of a single variable, it may be usefulto keep the number and values of other variables (i.e., the controlvariables) relatively constant from test to test (as can be seen in thecomparison between test promotions 102 a and 102 b).

Generally speaking, the test promotions may be generated using automatedtest promotion generation software 110, which varies for example thetest promotion variables and/or the values of the test promotionvariables and/or the number of the test promotion variables to come upwith different test promotions.

In the example of FIG. 2A, purposefully segmented subpopulations 104a-104 d are shown segmented using four segmentation criteria A, B, C, D,which may represent for example the age of the consumer, the householdincome, the zip code, and whether the person is known from pastpurchasing behavior to be a luxury item buyer or a value item buyer.These segmentation criteria are of course only illustrative and almostany demographics, behavioral, attitudinal, whether self-described,objective, interpolated from data sources (including past purchase orcurrent purchase data), etc. may be used as segmentation criteria ifthere is an interest in determining how a particular subpopulation wouldlikely respond to a test promotion. Further, although only a fewsegmentation criteria are shown in connection with subpopulations 104a-104 d in the example of FIG. 2A, segmentation may involve as many oras few of the segmentation criteria as desired. For example,purposefully segmented subpopulation 104 e is shown segmented using fivesegmentation criteria (A, B, C, D, and E).

In the present disclosure, a distinction is made between a purposefullysegmented subpopulation and a randomly segmented subpopulation. Theformer denotes a conscious effort to group individuals based on one ormore segmentation criteria or attributes. The latter denotes a randomgrouping for the purpose of forming a group irrespective of theattributes of the individuals. Randomly segmented subpopulations areuseful in some cases; however they are distinguishable from purposefullysegmented subpopulations when the differences are called out.

One or more of the segmentation criteria may vary from purposefullysegmented subpopulation to purposefully segmented subpopulation. In theexample of FIG. 2A, purposefully segmented subpopulation 104 a involvessegmentation criterion value A1 (representing a given attribute or rangeof attributes for segmentation criterion A) while purposefully segmentedsubpopulation 104 c involves segmentation criterion value A2(representing a different attribute or set of attributes for the samesegmentation criterion A).

As can be seen, different purposefully segmented subpopulation may havedifferent numbers of individuals. As an example, purposefully segmentedsubpopulation 104 a has four individuals (P1-P4) whereas purposefullysegmented subpopulation 104 e has six individuals (P17-P22). Apurposefully segmented subpopulation may differ from anotherpurposefully segmented subpopulation in the value of a singlesegmentation criterion (as can be seen in the comparison betweenpurposefully segmented subpopulation 104 a and purposefully segmentedsubpopulation 104 c wherein the attribute A changes from A1 to A2) or inthe values of many segmentation criteria simultaneously (as can be seenin the comparison between purposefully segmented subpopulation 104 a andpurposefully segmented subpopulation 104 d wherein the values forattributes A, B, C, and D are all different). Two purposefully segmentedsubpopulations may also be segmented identically (i.e., using the samesegmentation criteria and the same values for those criteria) as can beseen in the comparison between purposefully segmented subpopulation 104a and purposefully segmented subpopulation 104 b.

Also, there are no requirements that all purposefully segmentedsubpopulations must be segmented using the same number of segmentationcriteria (as can be seen in the comparison between purposefullysegmented subpopulation 104 a and 104 e wherein purposefully segmentedsubpopulation 104 e is segmented using five criteria and purposefullysegmented subpopulation 104 a is segmented using only four criteria)although for the purpose of validating the effect of a single criterion,it may be useful to keep the number and values of other segmentationcriteria (e.g., the control criteria) relatively constant frompurposefully segmented subpopulation to purposefully segmentedsubpopulation.

Generally speaking, the purposefully segmented subpopulations may begenerated using automated segmentation software 112, which varies forexample the segmentation criteria and/or the values of the segmentationcriteria and/or the number of the segmentation criteria to come up withdifferent purposefully segmented subpopulations.

In one or more embodiments, the test promotions are administered toindividual users in the purposefully segmented subpopulations in such away that the responses of the individual users in that purposefullysegmented subpopulation can be recorded for later analysis. As anexample, an electronic coupon may be presented in an individual user'scomputer-implemented account (e.g., shopping account or loyaltyaccount), or emailed or otherwise transmitted to the smart phone of theindividual. In an example, the user may be provided with an electroniccoupon on his smart phone that is redeemable at the merchant. In FIG.2A, this administering is represented by the lines that extend from testpromotion 102 a to each of individuals P1-P4 in purposefully segmentedsubpopulation 104 a. If the user (such as user P1) makes apromotion-significant response, the response is noted in database 130.

A promotion-significant response is defined as a response that isindicative of some level of interest or disinterest in the goods/servicebeing promoted. In the aforementioned example, if the user P1 redeemsthe electronic coupon at the store, the redemption is stronglyindicative of user P1 's interest in the offered goods. However,responses falling short of actual redemption or actual purchase maystill be significant for promotion analysis purposes. For example, ifthe user saves the electronic coupon in his electronic coupon folder onhis smart phone, such action may be deemed to indicate a certain levelof interest in the promoted goods. As another example, if the userforwards the electronic coupon to his friend or to a social networksite, such forwarding may also be deemed to indicate another level ofinterest in the promoted goods. As another example, if the user quicklymoves the coupon to trash, this action may also indicate a level ofstrong disinterest in the promoted goods. In one or more embodiments,weights may be accorded to various user responses to reflect the levelof interest/disinterest associated with the user's responses to a testpromotion. For example, actual redemption may be given a weight of 1,whereas saving to an electronic folder would be given a weight of only0.6 and whereas an immediate deletion of the electronic coupon would begiven a weight of −0.5.

Analysis engine 132 represents a software engine for analyzing theconsumer responses to the test promotions. Response analysis may employany analysis technique (including statistical analysis) that may revealthe type and degree of correlation between test promotion variables,subpopulation attributes, and promotion responses. Analysis engine 132may, for example, ascertain that a certain test promotion variable value(such as 2-for-1 discount) may be more effective than another testpromotion variable (such as 25% off) for 32-oz soft drinks if presentedas an electronic coupon right before Monday Night Football. Suchcorrelation may be employed to formulate a general population promotion(150) by a general promotion generator software (160). As can beappreciated from this discussion sequence, the optimization is aforward-looking optimization in that the results from test promotionsadministered in advance to purposefully segmented subpopulations areemployed to generate a general promotion to be released to the public ata later date.

In one or more embodiments, the correlations ascertained by analysisengine 132 may be employed to generate additional test promotions(arrows 172, 174, and 176) to administer to the same or a different setof purposefully segmented subpopulations. The iterative testing may beemployed to verify the consistency and/or strength of a correlation (byadministering the same test promotion to a different purposefullysegmented subpopulation or by combining the “winning” test promotionvalue with other test promotion variables and administering there-formulated test promotion to the same or a different set ofpurposefully segmented subpopulations).

In one or more embodiments, a “winning” test promotion value (e.g., 20%off listed price) from one test promotion may be combined with another“winning” test promotion value (e.g., packaged in plain brown paperbags) from another test promotion to generate yet another testpromotion. The test promotion that is formed from multiple “winning”test promotion values may be administered to different purposefullysegmented subpopulations to ascertain if such combination would eliciteven more desirable responses from the test subjects.

Since the purposefully segmented subpopulations are small and may besegmented with highly granular segmentation criteria, a large number oftest promotions may be generated (also with highly granular testpromotion variables) and a large number of combinations of testpromotions/purposefully segmented subpopulations can be executed quicklyand at a relatively low cost. The same number of promotions offered asgeneral public promotions would have been prohibitively expensive toimplement, and the large number of failed public promotions would havebeen costly for the manufacturers/retailers. In contrast, if a testpromotion fails, the fact that the test promotion was offered to only asmall number of consumers in one or more segmented subpopulations wouldlimit the cost of failure. Thus, even if a large number of these testpromotions “fail” to elicit the desired responses, the cost ofconducting these small test promotions would still be quite small.

In an embodiment, it is envisioned that dozens, hundreds, or eventhousands of these test promotions may be administered concurrently orstaggered in time to the dozens, hundreds or thousands of segmentedsubpopulations. Further, the large number of test promotions executed(or iteratively executed) improves the statistical validity of thecorrelations ascertained by analysis engine. This is because the numberof variations in test promotion variable values, subpopulationattributes, etc. can be large, thus yielding rich and granulated resultdata. The data-rich results enable the analysis engine to generatehighly granular correlations between test promotion variables,subpopulation attributes, and type/degree of responses, as well as trackchanges over time. In turn, these more accurate/granular correlationshelp improve the probability that a general public promotion createdfrom these correlations would likely elicit the desired response fromthe general public. It would also, over, time, create promotionalprofiles for specific categories, brands, retailers, and individualshoppers where, e.g., shopper 1 prefers contests and shopper 2 prefersinstant financial savings.

FIG. 2B shows, in accordance with an embodiment of the invention, thesteps for generating a general public promotion. In one or moreembodiments, each, some, or all the steps of FIG. 2B may be automatedvia software to automate the forward-looking promotion optimizationprocess. In step 202, the plurality of test promotions are generated.These test promotions have been discussed in connection with testpromotions 102 a-102 e of FIG. 2A and represent the plurality of actualpromotions administered to small purposefully segmented subpopulationsto allow the analysis engine to uncover highly accurate/granularcorrelations between test promotion variables, subpopulation attributes,and type/degree of responses in an embodiment, these test promotions maybe generated using automated test promotion generation software thatvaries one or more of the test promotion variables, either randomly,according to heuristics, and/or responsive to hypotheses regardingcorrelations from analysis engine 132 for example.

In step 204, the segmented subpopulations are generated. In anembodiment, the segmented subpopulations represent randomly segmentedsubpopulations. In another embodiment, the segmented subpopulationsrepresent purposefully segmented subpopulations. In another embodiment,the segmented subpopulations may represent a combination of randomlysegmented subpopulations and purposefully segmented subpopulations. Inan embodiment, these segmented subpopulations may be generated usingautomated subpopulation segmentation software that varies one or more ofthe segmentation criteria, either randomly, according to heuristics,and/or responsive to hypotheses regarding correlations from analysisengine 132, for example.

In step 206, the plurality of test promotions generated in step 202 areadministered to the plurality of segmented subpopulations generated instep 204. In an embodiment, the test promotions are administered toindividuals within the segmented subpopulation and the individualresponses are obtained and recorded in a database (step 208).

In an embodiment, automated test promotion software automaticallyadministers the test promotions to the segmented subpopulations usingelectronic contact data that may be obtained in advance from, forexample, social media sites, a loyalty card program, previous contactwith individual consumers, or potential consumer data purchased from athird party, etc. The responses may be obtained at the point of saleterminal, or via a website or program, via social media, or via an appimplemented on smart phones used by the individuals, for example.

In step 210, the responses are analyzed to uncover correlations betweentest promotion variables, subpopulation attributes, and type/degree ofresponses.

In step 212, the general public promotion is formulated from thecorrelation data, which is uncovered by the analysis engine from dataobtained via subpopulation test promotions. In an embodiment, thegeneral public promotion may be generated automatically using publicpromotion generation software which utilizes at least the test promotionvariables and/or subpopulation segmentation criteria and/or test subjectresponses and/or the analysis provided by analysis engine 132.

In step 214, the general public promotion is released to the generalpublic to promote the goods/services.

In one or more embodiments, promotion testing using the test promotionson the segmented subpopulations occurs in parallel to the release of ageneral public promotion and may continue in a continual fashion tovalidate correlation hypotheses and/or to derive new general publicpromotions based on the same or different analysis results. If iterativepromotion testing involving correlation hypotheses uncovered by analysisengine 132 is desired, the same test promotions or new test promotionsmay be generated and executed against the same segmented subpopulationsor different segmented subpopulations as needed (paths 216/222/226 or216/224/226 or 216/222/224/226). As mentioned, iterative promotiontesting may validate the correlation hypotheses, serve to eliminate“false positives” and/or uncover combinations of test promotionvariables that may elicit even more favorable or different responsesfrom the test subjects.

Promotion testing may be performed on an on-going basis using the sameor different sets of test promotions on the same or different sets ofsegmented subpopulations as mentioned (paths 218/222/226 or 218/224/226or 218/222/224/226 or 220/222/226 or 220/224/226 or 220/222/224/226).

FIG. 3A shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the user'sperspective. In step 302, the test promotion is received from the testpromotion generation server (which executes the software employed togenerate the test promotion). As examples, the test promotion may bereceived at a user's smart phone or tablet (such as in the case of anelectronic coupon or a discount code, along with the associatedpromotional information pertaining to the product, place of sale, timeof sale, etc.) or in a computer-implemented account (such as a loyaltyprogram account) associated with the user that is a member of thesegmented subpopulation to be tested or via one or more social mediasites. In step 304, the test promotion is presented to the user. In step306, the user's response to the test promotion is obtained andtransmitted to a database for analysis.

FIG. 3B shows in greater detail, in accordance with an embodiment of theinvention, the administering step 206 of FIG. 2 from the forward-lookingpromotion optimization system perspective. In step 312, the testpromotions are generated using the test promotion generation server(which executes the software employed to generate the test promotion).In step 314, the test promotions are provided to the users (e.g.,transmitted or emailed to the user's smart phone or tablet or computeror shared with the user using the user's loyalty account). In step 316,the system receives the user's responses and stores the user's responsesin the database for later analysis.

FIG. 4 shows various example segmentation criteria that may be employedto generate the purposefully segmented subpopulations. As show in FIG.4, demographics criteria (e.g., sex, location, household size, householdincome, etc.), buying behavior (category purchase index, most frequentshopping hours, value versus premium shopper, etc.), past/currentpurchase history, channel (e.g., stores frequently shopped at,competitive catchment of stores within driving distance), behavioraleconomics factors, etc. can all be used to generate with a high degreeof granularity the segmented subpopulations. The examples of FIG. 4 aremeant to be illustrative and not meant to be exhaustive or limiting. Asmentioned, one or more embodiments of the invention generate thesegmented subpopulations automatically using automated populationsegmentation software that generates the segmented subpopulations basedon values of segmentation criteria.

FIG. 5 shows various example methods for communicating the testpromotions to individuals of the segmented subpopulations being tested.As shown in FIG. 5, the test promotions may be mailed to theindividuals, emailed in the form of text or electronic flyer or couponor discount code, displayed on a webpage when the individual accesseshis shopping or loyalty account via a computer or smart phone or tablet.Redemption may take place using, for example, a printed coupon (whichmay be mailed or may be printed from an electronic version of thecoupon) at the point of sale terminal, an electronic version of thecoupon (e.g., a screen image or QR code), the verbal providing or manualentry of a discount code into a terminal at the store or at the point ofsale. The examples of FIG. 5 are meant to be illustrative and not meantto be exhaustive or limiting. One or more embodiments of the inventionautomatically communicate the test promotions to individuals in thesegmented subpopulations using software thatcommunicates/email/mail/administer the test promotions automatically. Inthis manner, subpopulation test promotions may be administeredautomatically, which gives manufacturers and retailers the ability togenerate and administer a large number of test promotions with lowcost/delay.

FIG. 6 shows, in accordance with an embodiment, various examplepromotion-significant responses. As mentioned, redemption of the testoffer is one strong indication of interest in the promotion. However,other consumer actions responsive to the receipt of a promotion may alsoreveal the level of interest/disinterest and may be employed by theanalysis engine to ascertain which test promotion variable is likely orunlikely to elicit the desired response. Examples shown in FIG. 6include redemption (strong interest), deletion of the promotion offer(low interest), save to electronic coupon folder (mild to stronginterest), clicked to read further (mild interest), forwarding to selfor others or social media sites (mild to strong interest). As mentioned,weights may be accorded to various consumer responses to allow theanalysis engine to assign scores and provide user-interest data for usein formulating follow-up test promotions and/or in formulating thegeneral public promotion. The examples of FIG. 6 are meant to beillustrative and not meant to be exhaustive or limiting.

FIG. 7 shows, in accordance with an embodiment of the invention, variousexample test promotion variables affecting various aspects of a typicaltest promotion. As shown in FIG. 7, example test promotion variablesinclude price, discount action (e.g., save 10%, save $1, 2-for-1 offer,etc.), artwork (e.g., the images used in the test promotion to drawinterest), brand (e.g., brand X potato chips versus brand Y potatochips), pricing tier (e.g., premium, value, economy), size (e.g., 32 oz,16 oz, 8 oz), packaging (e.g., single, 6-pack, 12-pack, paper, can,etc.), channel (e.g., email versus paper coupon versus notification inloyalty account). The examples of FIG. 7 are meant to be illustrativeand not meant to be exhaustive or limiting. As mentioned, one or moreembodiments of the invention involve generating the test promotionsautomatically using automated test promotion generation software byvarying one or more of the test promotion variables, either randomly orbased on feedback from the analysis of other test promotions or from theanalysis of the general public promotion.

FIG. 8 shows, in accordance with an embodiment of the invention, ageneral hardware/network view of the forward-looking promotionoptimization system 800. In general, the various functions discussed maybe implemented as software modules, which may be implemented in one ormore servers (including actual and/or virtual servers). In FIG. 8, thereis shown a test promotion generation module 802 for generating the testpromotions in accordance with test promotion variables. There is alsoshown a population segmentation module 804 for generating the segmentedsubpopulations in accordance with segmentation criteria. There is alsoshown a test promotion administration module 806 for administering theplurality of test promotions to the plurality of segmentedsubpopulations. There is also shown an analysis module 808 for analyzingthe responses to the test promotions as discussed earlier. There is alsoshown a general population promotion generation module 810 forgenerating the general population promotion using the analysis result ofthe data from the test promotions. There is also shown a module 812,representing the software/hardware module for receiving the responses.Module 812 may represent, for example, the point of sale terminal in astore, a shopping basket on an online shopping website, an app on asmart phone, a webpage displayed on a computer, a social media newsfeed, etc. where user responses can be received.

One or more of modules 802-812 may be implemented on one or moreservers, as mentioned. A database 814 is shown, representing the datastore for user data and/or test promotion and/or general publicpromotion data and/or response data. Database 814 may be implemented bya single database or by multiple databases. The servers and database(s)may be coupled together using a local area network, an intranet, theinternet, or any combination thereof (shown by reference number 830).

User interaction for test promotion administration and/or acquiring userresponses may take place via one or more of user interaction devices.Examples of such user interaction devices are wired laptop 840, wiredcomputer 844, wireless laptop 846, wireless smart phone or tablet 848.Test promotions may also be administered via printing/mailing module850, which communicates the test promotions to the users via mailings852 or printed circular 854. The example components of FIG. 8 are onlyillustrative and are not meant to be limiting of the scope of theinvention. The general public promotion, once generated, may also becommunicated to the public using some or all of the user interactiondevices/methods discussed herein.

Test promotion results are sometimes influenced by events that occur inthe same time frame (but not necessarily has to be, although can be, atthe same time) as the test promotion. For example, if a store hasheavily discounted a given item for weeks, consumer demand for that itemmay have been temporarily sated for a while before the consumers whocustomarily shop at that store are ready to purchase that item again. Ifa test promotion for that item is executed in the time periodimmediately following the heavy discounting of that item by the store,it is likely that the decrease in enthusiasm in response to that testpromotion may be due to the fact that the consumer has already stockedup on that item and not due to the design of the test promotion.

Promotions for products and/or services are often produced by marketers.For example, when a manufacturer has a product to promote, marketing andadvertising personnel typically work together to create a promotioncampaign to grab the consumer's attention and/or to provide an incentive(such as a certain percentage off coupon incentive) to spur the purchaseof the product or service. The resulting promotion may be, for example,an electronic coupon for 5% off the purchase price of a particularquantity of the product, with the electronic coupon having specificaction words (e.g., “buy now”) and graphical elements and look-and-feeland provided on a specific channel (e.g., email, mobile, social mediasites, etc.).

Up to now, this promotion creation process that results in a displayablepromotion has relied almost exclusively on the experience and intuitionof the promotion creators (e.g., the advertising and/or marketingpersonnel responsible for creating the promotion). Different promotioncreators may have different proposals for the promotion (e.g., theaforementioned electronic coupon), since each carries with him or herdeeply personal/professional experience and bias with various types ofpromotions. For example, some promotion creators may prefer a vibrantlook, while others may prefer a more subdued look. As another example,some promotion creators may boldly state the action words as part of thelayout (e.g., “Buy now”), while others may prefer a soft-sell approach.

If the proposed displayable promotion is approved by managementpersonnel of the manufacturer, the promotion creator may then create thedisplayable promotion to be offered to the public via various channels,and data may be obtained during the promotion campaign to test theeffectiveness of the displayable promotion employed. If that displayablepromotion turns out to be ineffective, that displayable promotion may besuspended and another displayable promotion may be tried instead.

However, the promotion creation process of the prior art still involveshuman qualitative judgment and is thus prone to errors, especially ifthe human personnel involved are not skilled at designing a displayablepromotion that would likely receive a positive response from theconsumers. Skilled human help is also expensive and difficult to findand is a limited resource, thereby representing a bottleneck in thepromotion creation process.

Still further, since it is not known in advance how well the proposeddisplayable promotion would perform (e.g., in term ofreturn-on-investment, in term of coupon redemption rate, or in term ofimproved sales volume), decision makers lack information to decide in aninformed manner whether the proposed promotion campaign should beproceed.

What is desired is a more automated and systematic approach to promotioncreation such that the proposed displayable promotion would have ahigher likelihood of success based on provided criteria. What is alsodesired is an automatic and systematic approach to predicting the resultof such proposed displayable promotion so that decision makers wouldhave more information to decide, in an informed manner, regarding themerit of a proposed displayable promotion.

FIG. 9 shows, in accordance with an embodiment, a conceptual high-levelview of the promotion forecaster 910. Variable values 912 for thepromotion concept are provided to promotion forecaster 910. For example,if the item to be promoted is a shirt, the variables may be brand,color, size, and gender (X, Y, Z, and G respectively). Each variable mayhave one or multiple values (as in the case the promoter has multipleshirts of different colors or brands to promote).

Other inputs to promotion forecaster 910 are also possible. An exampleinput may represent the channels desired (e.g., how/where the consumercan access the promotion and may include channels such as social mediasites, in-video placement such as the case with Youtube™ advertisements,emails, mobile browser, the consumer's own loyalty account, etc.).Another input may represent the criteria for selecting proposedpromotions (e.g., whether to enhance sales volume, to enhanceprofitability, to increase awareness, etc.). Another input may be thetypes of promotion desired (e.g., % off, $ off, buy-one-get-one-free).In general, any number of criteria can be inputted into promotionforecaster 910 to more specifically indicate the type of promotionsdesired.

Promotion forecaster 910 works cooperatively with Experiment KnowledgeBase (EKB) 920 and automatically produces proposed promotions 930, 932,and 934. These are displayable promotions designed to be executed on thechannels desired for the item whose variable values 912 are entered intopromotion forecaster 910.

“Automated” or “automatic” or “automatically” in the context ofproposing promotions and predicting results denotes without requiredhuman intervention but does not require zero human intervention. Theterm however denotes that proposing promotions and predicting results inaccordance with embodiments of the invention can be executed withouthuman intervention if desired. However, embodiments of the invention donot exclude the optional participation of humans, especially experts, invarious phases of process if such participation is optionally desired atvarious points to inject human intelligence or experience or timing orjudgment into the process. Further, the term does not exclude theoptional nonessential ancillary human activities that can otherwise alsobe automated (such as for example issuing the “run” command to begin theprocess or issuing the “send” command to send the proposed promotionsonward).

As shown, each of proposed promotions 930, 932, and 934 may have adifferent format and may be of a different type. For example, proposedpromotion 930 is of the “% off” variety; proposed promotion 932 is ofthe “$ off” variety, and proposed promotion 934 is of the“buy-one-get-one-free” variety. Although only 3 are shown, any number ofproposed promotions may be presented

In the case of FIG. 9, proposed promotions 930, 932, and 934 are rankedaccording to volume lift, which is the criteria for proposed promotionselection in this example. However, any other criteria may be specified,if desired. As can be seen, proposed promotion 930 results in a 25%increase in volume lift (e.g., increased sales volume) over the baselinepromotion and a ROI (return on investment) of 25%. Proposed promotion932 results in a 20% increase in volume lift over the baseline promotionand a ROI of 30%. Proposed promotion 934 results in a 15% increase involume lift over the baseline promotion and a ROI of 35%. The financialdata, which is outputted by promotion forecaster 910 and shown in FIG.9, are only examples of the types of forecasted result that can beproduced for each proposed promotion.

Insights 940 represent the explained rationale for the selection of theproposed promotions 930, 932, and 934. These insights are variable levelinsights and apply across multiple or all promotions. By isolating andanalyzing the contribution from each variable value (e.g., B2 or C4) inpast promotions, it is possible to ascertain that if a certaincombination of variable values is chosen, positive responses due to theconstituent variable values can be expected. Examples of such positiveresponses are shown as insights 940 in FIG. 9 with respect to variablevalues for brand, color, and size.

One of the reasons it is possible to, in an automated manner and withimproved accuracy, forecast the promotions that would likely besuccessful, along with the projected financial result, is the use ofexperimental knowledge base 920. Although databases storing pastpromotions and their financial impact exist, experimental knowledge base920 represents a database that collects and stores specific types ofexperimental promotion data that make it possible to automaticallyanalyze and propose displayable promotions that would have a higherlikelihood of success based on provided criteria as well as to predictthe result of such proposed displayable promotions.

FIG. 10 shows an example experimental knowledge base 1002. Theillustration of FIG. 10 is intended to be conceptual and not reflectiveof the actual database table or data schema, which may vary depending onthe database technology chosen and vendors. Experimental knowledge base1002 stores not only past multivariate promotions 1010 a, 1010 b, 1010c, 1010 d, 1010 e, and 1010 f but also their performance (e.g.,financial performance or performance in accordance to any desiredcriteria).

More importantly, since this is a data store of experimental promotionsthat were conducted under controlled conditions (such as thoseautomatically generated and administered to small groups of consumers totest promotions to obtain information for deriving the generalizedpublic promotion), the baseline experiment information is also known andtracked. With respect to FIG. 10, promotions 1010 b, 1010 c, 1010 d,1010 e, and 1010 f employ different values for variables X, Y, and Z,and those combinations differ from those values employed in controlbaseline experiment 1010 a. For example, promotion 1010 b employsvariable values X1, Y2, Z1 whereas control promotion 1010 a employsvariable values X1, Y1, Z1. The designation of the control experiment istypically done by the promotion designer of the experiment involvingpromotions 1010 a, 1010 b, 1010 c, 1010 d, 1010 e, and 1010 f.

Preferably, the control experiment 1010 a and the variations (1010b-1010 f) are executed at the same time period t1 to subject all theseexperimental promotions (1010 a and 1010 b-1010 f) to the same testingenvironment during that time period. Alternatively or additionally, allthese experimental promotions (1010 a and 1010 b-1010 f) are preferablycarried out in a manner so as to keep the testing environment (e.g.,location, type of consumers, channels, etc.) as similar as possible. Assuch, external covariates that may affect the results (such as forexample unemployment rate, weather, the existence of other promotions incompeting stores, etc.) affect the promotion results similarly acrosspromotions, thus tending to zero-out the contribution of the externalcovariates when the promotion results are compared against one another.

The existence of a database that stores the baseline experiment andvariations of multivariate promotions that only vary the variable valuesbut administered in substantially the same testing environment (i.e.,same city, same neighborhood, same time period, etc.) or whose resultscan be adjusted to account for different testing environments providethe basis for the prediction tool in accordance with embodiments of theinvention.

Without this baseline experiment information and without the existenceof variations that only differ in variable values, and/or without thetesting methodology that is designed to zero-out the covariates or canbe the basis for computationally zeroing out the covariates with areasonable degree of accuracy, it would have been impossible toaccurately compare promotions to determine which promotion has betterperformance due only (or primarily) to a difference in variable values.Without this comparison, forecasting a proposed promotion based on pastperformance of other promotions would have been impossible. Further,without this baseline experiment information, it also would have beenimpossible to assess the performance of the various promotions and thuswould have been impossible to forecast with any reasonable degree ofaccuracy the performance of a proposed promotion if one or more of thevariable values in the variations is used for the proposed promotion.

Items 1012 a shows another baseline experiment at time period t2, anditems 1012 b-1012 d represent variations of the baseline experiment thattest different combinations of variables X, Y, and Z. Item 1012 a servesas the baseline experiment against which the performance of promotions1012 b-1012 d can be measured.

FIG. 11 shows, in accordance with an embodiment of the invention,promotion forecaster 910 of FIG. 9 in greater detail. Promotionforecaster 910 includes classifier 1102, representing the module forclassifying the promotional concept received (e.g., the promotionalconcept represented by variable values inputted into promotionforecaster 910 in FIG. 9). The goal of classification is to assess theclassifications of the variables associated with variable values for thepromotional concept provided to promotion forecaster 910. In the contextof the example of FIG. 9, information inputted may be classified intotype of promotion (e.g., % discount versus $ off discount versusbuy-one-get-one-free), the gender of the person in the image, the factthat the image is human or non-human, the format of the promotion, thechannel of the promotion, etc.

Classification may also be done in accordance with behavioral economicprinciple. An example of a behavioral economic principle may be found inDaniel Kahneman's well-known work in codifying a number of heuristics orprinciples describing types of human behavior as a response to economic,psychological and social situations. An example would be people'stendency to be impressed by large numbers within an advertisement orpromotion. “Over 50 Million served daily” or “Join the already 20Million of subscribers today!” All of these contain large numbers toimpress the consumer into being persuading or influenced. Such a humanresponse follows the Numerosity principle.

Other classifications may also be possible, depending on for example theindustry involved and type of merchandise. In an embodiment,classification may be learned—that is machine learning can be applied tothe promotion variables of promotions stored in the database in order tofor example automatically cluster or learn common attributes from whichclassifications can be generated. Since the classification labels arealmost unlimited depending on for example, industry, goal, type ofproduct or service, etc., it should be understood that generallyspeaking, classification abstracts the variables and creates a higherlevel of abstraction for comparison purposes. Comparison will bediscussed later herein.

After classification, the promotional concept may be normalized.Normalization may be necessary to obtain more abstract metrics to enablecomparison of promotions across different domains. For example, althoughthe item to be promoted may be a teenage or young adult sweater, it ispossible that successful promotions for adult men shirts in the past mayhave elements (i.e., variable values) which, if incorporated, may resultin an increased likelihood of success for the proposed promotions foryouth sweaters. As another example, although the item to be promoted maybe an article of clothing, it is possible that successful promotions forcosmetics in the past may have elements that, if incorporated, mayresult in an increased likelihood of success for the proposed promotionsfor clothing.

In one or more embodiments, normalization further abstracts theattributes of the promotional concept into abstracts metrics that may beshared by past promotions stored in experimental knowledge base 920.Normalization may be thought of as standardization to a more abstractlevel so there would be a larger pool of promotions from which to pickthe winning variable values or combination of variable values. Oncenormalized, past promotions sharing such abstract metrics may beexamined such that variable values that performed the best (known sincethis is a database of multivariate experiments and their results) may beselected for use in the proposed promotion.

For example, promotion components (such as font, wording, font size) canbe normalized across promotions into a higher abstraction to facilitatecomparison. As another example, imagery may be normalized using, forexample, well-developed techniques for describing or assigningattributes to images.

As a specific example, an image of a man wearing a sweater in apromotion concept for shoes (i.e., part of what is inputted intopromotion forecaster 910) may be normalized or abstracted to “malegender” image to enable the comparison of the contributions by malegender images in promotions that contain the male gender image. If theimage of a man walking a dog in a pharmaceutical promotion (i.e., apromotion in a different domain that contains the male gender image) wasfound in past experiments to be the variable value that contributespositively to the result of that test promotion, that image of a manwalking a dog may be selected for use in the proposed promotion forshoes.

It should be noted that in this example, the abstraction level is “malegender” image although it is also possible to abstract higher to “humanbeing present” image. The exact level of normalization may be tuned andmay represent one of the control knobs for determining the size of thepool of past promotions from which the proposed promotions may beformulated.

A similar analysis may be made for the action word (e.g., “get”) in theshoe promotion concept. Normalizing to “action word” may enable thecomparison of the contributions made by other action words (e.g., “get”,“grab”, “buy”, “acquire”) in the promotions that contain the actionwords. If the action word “grab” in a carpet promotion (i.e., apromotion in a different domain that contains action words) was found inpast experiments to be the variable value that contributes positively tothe result of that test promotion, that action word “grab” may beselected for use in the proposed promotion for shoes.

Other variable values of the promotion concept inputted into promotionforecaster 910 may be similarly analyzed to arrive at the completeproposed promotion having the predicted optimal values for the promotionvariables.

Prediction of result (including for example financial impact) of theproposed promotion is also possible. Normalization of product type, unitsize, volume sold, cost of good may be made such that comparisonsbetween past promotions are possible although they involve differentproduct types, different unit sizes, different volumes sold, etc. As anexample, weights may be assigned to a promotion for a product that soldin the millions of units such that the volume sold can be normalized andcompared against a product that sold in the thousands of units. Once thevolume sold is normalized, these two promotions may be compared toascertain which has a higher return on investment, for example.

Once normalized, a predicted financial metrics (such as predictedpercentage lift) can be found for a particular variable value of aparticular past promotion (since the percentage lift for that promotionwas recorded and analysis against the baseline promotion and otherpromotions of that multivariate experiment would yield the percentagelift contributed by a particular variable value). Accordingly,embodiments of the invention not only can suggest the variable values touse in the proposed promotion but also the predicted result. For thevarious variable values of the combination that make up the proposedpromotion, the associated predicted results may be aggregated using somestatistical or weighting techniques to arrive at the predicted resultfor the combination of proposed variable values in the proposedpromotion.

It should be noted that one or more proposed promotions may be generatedand may be ranked in accordance with some predefined criteria, ifdesired. In other words, given a certain criteria (e.g., increased salesvolume), the steps of the invention may be iterated through to generatemultiple different proposed promotions and to rank them in accordancewith the furnished criteria, if desired.

In one or more embodiments, the same analysis may be employed togenerate an experimental test plan to test variable values andcombinations predicted to be likely to be successful. Thus in thecontext of the sweater example, the recommendation for font may bemultiple high performing fonts, each of which has been found to be animportant factor in the success of at least one experimental promotionin the past; the recommendation for graphics may be multiple highperforming pictures, each of which has been found to be an importantfactor in the success of at least one experimental promotion in thepast; the recommendation for background color may be multiple highperforming background colors, each of which has been found to be animportant factor in the success of at least one experimental promotionin the past. These combinations form the pool of variable values for anautomated promotion testing plan to explore various combinations ofthese variable values.

It should be noted that embodiments of the invention also enablepredicting the result of a promotion if the user inputs a specificcombination of variable values for the proposed promotion. In otherwords, in the case when the user knows exactly the promotion to bepushed to the consumers, the capability to predict result as discussedabove may be used to predict the result of that specific proposedpromotion, giving the decision makers data to decide whether to proceedwith the promotion.

Recommender 1106 of FIG. 11 represents the module that obtains thevariable values from previous promotions, each variable valuerepresenting a recommended component of the proposed promotion thatwould likely be successful. Recommender 1106 thus may recommend variablevalues B5, C3, S7, G:=female for the proposed promotion for the exampleof FIG. 9 since each of values B5, C3, S7, and G9 was found to be highperforming in past promotions (which may be different promotions fromdifferent domains). Machine modeling and/or machine learning may beemployed for this analysis, in one or more embodiments.

Further, recommender 1106 also provides the predicted result for each ofvariable values B5, C3, S7, G=female, based on result data of pastpromotions, and may provide an aggregate result for the proposedpromotion that combines variable values B5, C3, S7, G=female. Again,machine modeling and/or machine learning may be employed for thisanalysis, in one or more embodiments.

Returning to FIG. 10, experimental knowledge base 1002 may includeongoing, updated, classified, and/or normalized experimental data onpromotions. For example, classification data pertaining to priorpromotion experiments may be kept by experimental knowledge base 1002.As another example, normalized data pertaining to prior promotionexperiments may be kept by experimental knowledge base 1002.

As another example, metadata pertaining to prior promotion experimentsmay be kept by experimental knowledge base 1002. These metadata mayinclude, for example and without limitation, the experimental conditionssuch as who conducted the experiments, where the experiments weretested, how much data was collected, what was the control baselineexperiment, etc.

As another example, covariate data pertaining to prior promotionexperiments may be kept by experimental knowledge base 1002. Thecovariate data may include, for example and without limitation, dataregarding extraneous conditions that exist when the prior promotionexperiments were conducted. Example covariates include data regardingwhether the promotion was conducted during the holiday season, whetherthe weather was cold or warm, whether the promotion was conducted duringvalentine, the interest rate, the stock market index, prevailingconsumer moods, etc.

As another example, promotion variable values pertaining to priorpromotion experiments may be kept by experimental knowledge base 1002.These data items may include, for example, the variable values (e.g.,font size, graphics, action words, type of discount, item involved)and/or other attributes of the prior test promotions.

As another example, promotion response data pertaining to priorpromotion experiments may be kept by experimental knowledge base 1002.The promotion response data may include, for example and withoutlimitation, the number of people who loaded the promotion, how manyactually clicked on the promotion, how many actually redeemed, etc. If apromotion stored in the experimental knowledge base has not actuallybeen conducted, this promotion response data may be predicted data, inone or more embodiments.

As another example, result data pertaining to prior experimentalpromotions may be kept by experimental knowledge base 1002. The resultdata may include, for example and without limitation, the volume liftdue to the promotion, the return on investment, etc. If a promotionstored in the experimental knowledge base has not actually beenconducted, this result data may be predicted data, in one or moreembodiments.

Other data items may also be kept by experimental knowledge base 1002.The important point is the database contains description and performancedata of past multivariate experimental promotions that were conducted ina controlled manner. Further, there is also included informationpertaining to the baseline experiment so that recommendations based onclassified and/or normalized promotion concept data and past promotiondata may be automatically generated. In one or more embodiments, datapertaining to the current promotional concept being investigated mayalso be updated into experimental knowledge base 1002 if desired.

FIG. 12 shows, in accordance with an embodiment of the invention, thesteps for generating, in an automated manner and with improved accuracy,a promotion that has an improved likelihood of success based on dataobtained from past experimental promotions. In step 1202, promotionconcept data (such as for example promotion variable values, channels,etc.) is provided to a classifier module of the promotion generatorengine.

In step 1204, the classifier module of the promotion forecaster engineautomatically classifies the promotion concept data intoclassifications.

In step 1206, the normalizer module of the promotion forecaster engineautomatically normalizes the promotion concept data to enablecomparisons with and among prior experimental promotions stored in theexperimental knowledge database. As discussed in connection with FIG.10, this experimental knowledge base having experimental data formultivariate promotion experiments that are collected in a controlledmanner and having baseline experiment information holds an important keyin enabling the robust and automated generation of proposed promotions.

In step 1208, one or more proposed promotions are automaticallygenerated by the recommender module of the promotion forecaster engine.The proposed promotions are generated from analysis of the normalizedpromotion concept data and from previously stored experimental promotiondata. Predicted result may also be provided in step 1208 if desired.

As can be appreciated from the foregoing, embodiments of the inventionenable the automated and robust generation of proposed promotions basedon promotion concept input and based on a database of previouslycollected multivariate experimental promotions that were conducted undercontrolled conditions. By classifying, normalizing, and then performingcomparisons of past experimental promotions in the same domain andacross domains, embodiments of the invention are able to automaticallygenerate proposed promotions from variable values, each of which hasproven to be successful in past promotions. Further, embodiments of theinvention also extract and compute associated result data to present tohuman decision makers, enabling the human decision makers to decidewhether a proposed promotion is worth pursuing in the marketplace.

While this invention has been described in terms of several preferredembodiments, there are alterations, permutations, and equivalents, whichfall within the scope of this invention. The invention should beunderstood to also encompass these alterations, permutations, andequivalents. It should also be noted that there are many alternativeways of implementing the methods and apparatuses of the presentinvention. Although various examples are provided herein, it is intendedthat these examples be illustrative and not limiting with respect to theinvention.

1. A computer-implemented method for performing promotion optimization,comprising: generating a plurality of test promotions; administering theplurality of test promotions to a plurality of segmented subpopulationsof consumers; obtaining responses from said segmented subpopulations ofconsumers; and generating a general population promotion responsive toanalysis of said responses, wherein the plurality of test promotions areautomatically proposed.